In this paper, we perform several experiments focusing on the problems of environment recognition from audio particularly for forensic application. We investigated the effect of temporal zero crossing fea ture and some selected MPEG-7 audio low level descriptors on environment sound recognition. The performance is evaluated against varying number of training sounds and samples per each training file. Experimental results show that higher recognition accuracy is achieved by increasing the number of training files and by decreasing the number of samples per training file.
Problem statement: This study investigated zero crossing features and selected MPEG-7 audio descriptors for environment sound recognition applications such as audio forensics. Approach: The study implemented several experiments focusing on the problems of environment recognition from audio particularly for forensic applications. Results: It was investigated the effect of the temporal zero crossing feature as well as selected MPEG-7 audio low level descriptors on environment sound recognition. The performance was evaluated against a varying number of training sounds and samples per training file. Conclusion/Recommendations: Experimental results showed that higher recognition accuracy is achieved by increasing the number of training files and by decreasing the number of samples per training file. This study presented an audio environment recognition using zero crossing features and MPEG-7 Descriptors.
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